license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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mit | [] | false | 1. ```python import requests import torch from PIL import Image from transformers import AutoModel, AutoProcessor repo = "Bingsu/clip-vit-base-patch32-ko" model = AutoModel.from_pretrained(repo) processor = AutoProcessor.from_pretrained(repo) url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Im... | e675d52aa9f091f4c81b22eacf60ec7c |
mit | [] | false | 2. ```python from transformers import pipeline repo = "Bingsu/clip-vit-base-patch32-ko" pipe = pipeline("zero-shot-image-classification", model=repo) url = "http://images.cocodataset.org/val2017/000000039769.jpg" result = pipe(images=url, candidate_labels=["고양이 한 마리", "고양이 두 마리", "분홍색 소파에 드러누운 고양이 친구들"], hypothesis... | 9425c8776d2735820fcc4e00dcd80263 |
mit | [] | false | Tokenizer 토크나이저는 한국어 데이터와 영어 데이터를 7:3 비율로 섞어, 원본 CLIP 토크나이저에서 `.train_new_from_iterator`를 통해 학습되었습니다. https://github.com/huggingface/transformers/blob/bc21aaca789f1a366c05e8b5e111632944886393/src/transformers/models/clip/modeling_clip.py | 2cdf4778ffa90289ff8775edac80922a |
mit | [] | false | casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 pooled_output = last_hidden_state[ torch.arange(last_hidden_state.shape[0]), input_ids.to(torch.int).argmax(dim=-1) ] ``` CLIP 모델은 `pooled_output`을 구할때 id가 가장 큰 토큰을 사용하기 때문에, eos 토큰은 가장 마지막 토큰이 되... | 944c38b8d2c1060d44d2a3fab8a5552d |
apache-2.0 | ['multiberts', 'multiberts-seed_16'] | false | MultiBERTs - Seed 16 MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variatio... | 3315caedfb4b89ce7b1d8f7304d5c182 |
apache-2.0 | ['multiberts', 'multiberts-seed_16'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_16') model = TFBertModel.from_pretrained("google/multiberts-seed_... | 3f489c8b54a804143d5ccf4e4f762ea6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2776 - F1: 0.8303 | 7d29516da8ee3b242eae253ea6b93f6b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5895 | 1.0 | 191 | 0.3318 | 0.7894 | | 0.263 | 2.0 | 382 | 0.2873 | 0.8175 | | 0.1782 | 3.0 | 573 | 0.2776 | 0.8303 | ... | 6b4ea8bb0b36bf5ec569372b9d39d65e |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Indonesian-GSD | Feature | Description | | --- | --- | | **Name** | `id_udv25_indonesiangsd_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimenta... | 5acb83cf0700cb3868e17dbc29491dc1 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (1325 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `APP`, `ASP`, `ASP+PS2`, `ASP+PS3`, `ASP+T--`, `ASS`, `ASS+PS3`, `B--`, `B--+PS3`, `B--+T--... | 239d5f3e5346b65b61679801b45496ab |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.99 | | `TOKEN_P` | 99.98 | | `TOKEN_R` | 99.99 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 92.98 | | `SENTS_P` | 92.40 | | `SENTS_R` | 93.56 | | `TAG_ACC` | 94.79 | | `POS_ACC` | 93.17 | | `MORPH_ACC` | 95.90 | | `DEP_UAS` | 86.16 | | `DEP_LAS` | 78.38 | | `LEMMA_... | 6c21976c55a31c674dbf8138f96db6bf |
cc-by-sa-4.0 | ['ainu', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Ainu texts (in カタカナ, Roman, and Кириллица) for POS-tagging and dependency-parsing, derived from [deberta-base-ainu](https://huggingface.co/KoichiYasuoka/deberta-base-ainu). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal ... | 0777f345a4ed45e2db6cbf227be52e36 |
cc-by-sa-4.0 | ['ainu', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-ainu-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-ainu-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasu... | 9a82669245d6470559f3c9fbc18e4351 |
apache-2.0 | ['generated_from_trainer'] | false | convnext-tiny-224-klobasaniklobasa This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on a small dataset of klobasa images scraped from the internets. It achieves the following results on the evaluation set: - Loss: 0.4401 - Accuracy: 0.8958 | 218c8a856f26a702e6078560961f002e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 | c40196918be2d2002a623bf05592b6b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7062 | 1.0 | 48 | 0.7116 | 0.8438 | | 0.4831 | 2.0 | 96 | 0.5968 | 0.8333 | | 0.2429 | 3.0 | 144 | 0.5384 | 0.... | 3b9a67cc5fb68b7b410bbcecfc8452cc |
apache-2.0 | ['generated_from_trainer'] | false | platzi-vit-model-javi-javiai This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Accuracy: 0.9774 | 3469f9e23d574d35c2c8f0efd6f392d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 57531581b77d7c414980f3319b8e78a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0532 | 3.85 | 500 | 0.0623 | 0.9774 | | 497bb91ea8f4fc841509a2e3592b5835 |
apache-2.0 | ['translation'] | false | opus-mt-ha-fr * source languages: ha * target languages: fr * OPUS readme: [ha-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ha-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 23450703f4043527e4c5ba87937318c4 |
apache-2.0 | ['generated_from_trainer'] | false | find-mention-pos-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9116 | e4c5619b016f04371e36077158f1d89c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 97 | 0.9116 | | No log | 2.0 | 194 | 0.9116 | | No log | 3.0 | 291 | 0.9116 | | 8ed856d7ebb11c6ca4ffa6c11668fa4b |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-cnn-arxiv-pubmed-v3-e12 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8157 - Rouge1: 56.7429 - Rouge2: 41.0185 - Roug... | 1a0ff74bea3f31ec3d44111ff489f3c1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.5037 | 1.0 | 795 | 1.0815 | 52.4727 | 33.4915 | 35.3774 | 50.1955 | ... | b8a0c77fa94c63eeff3ae580d05b9efa |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e10 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8234 - R... | d7ee3d68bd7734d3c891a34eb071087c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | 85909249aacb74053ad4d77f3dabee1a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.8670 | 53.2875 | 33.7336 | 36.1194 | 50.6842 | ... | 735fcba171f0a73884196980dafff9cf |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0848 | 18c6e8deed6a9b882fdcee8fec3226b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0337 | 1.0 | 5546 | 1.0150 | | 0.7546 | 2.0 | 11092 | 1.0015 | | 0.5537 | 3.0 | 16638 | 1.0848 | | 3d7924f635908f1886a18319488d0e65 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | flan-t5-base3 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0424 - Rouge1: 18.1411 - Rouge2: 17.0579 - Rougel: 18.1468 - Rougelsum: 18.1284 | b6093c5676de2e525936058513ec275d |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | 46c7adfe0efcb80a5877f758d38bff9d |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | No log | 1.0 | 208 | 0.0548 | 18.1442 | 17.0639 | 18.1479 | 18.1291 | | No log | 2.0 ... | 1fc1fc1eef2383c21baa166123e18ffb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2125 | 77a10618716d06aac4ce0b09a9397587 |
other | [] | false | Cool Japan Diffusion 2.1.2 Beta Model Card  [注意事项。中国将对图像生成的人工智能实施法律限制。 ](http://www.cac.gov.cn/2022-12/11/c_1672221949318230.htm) （中国国内にいる人への警告） English version is [here](README_en.md). | 5f5cae7e9555dab4ba91483cd18b1c2a |
other | [] | false | 使い方 手軽に楽しみたい方は、こちらの[Space](https://huggingface.co/spaces/aipicasso/cool-japan-diffusion-latest-demo)をお使いください。 詳しい本モデルの取り扱い方は[こちらの取扱説明書](https://alfredplpl.hatenablog.com/entry/2023/01/11/182146)にかかれています。 モデルは[ここ](https://huggingface.co/aipicasso/cool-japan-diffusion-2-1-2-beta/resolve/main/v2-1-2-beta.ckpt)からダウンロードできま... | 9b75e4a092b3db9afd5d0c5a03e6f48b |
other | [] | false | Web UIの場合 **xformersをインストールし、--xformers --disable-nan-checkオプションをオンにすることをおすすめします。そうでない場合は--no-halfオプションをオンにしてください** こちらの[取扱説明書](https://alfredplpl.hatenablog.com/entry/2023/01/11/182146)に従って作成してください。 | d24e8a67d02ed7a3b9e20581661f8853 |
other | [] | false | Diffusersの場合 [🤗's Diffusers library](https://github.com/huggingface/diffusers) を使ってください。 まずは、以下のスクリプトを実行し、ライブラリをいれてください。 ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` 次のスクリプトを実行し、画像を生成してください。 ```python from diffusers import StableDiffusionPipelin... | b205d9140f582f8dd3f43ba9a92bd9e3 |
other | [] | false | 学習 **学習データ** 次のデータやモデルを主に使ってStable Diffusionをファインチューニングしています。 - VAEについて - DanbooruやDanbooru datasetを除いた日本の国内法を遵守したデータ: 65万種類 （データ拡張により無限枚作成） - U-Netについて - DanbooruやDanbooru datasetを除いた日本の国内法を遵守したデータ: 200万ペア - マージしたモデル: 1つ (Open RAIL ライセンス) **学習プロセス** Stable DiffusionのVAEとU-Netをファインチューニングしました。 - **ハード... | b21e5b07a758ae2d728eadfcec3d48e3 |
apache-2.0 | ['translation'] | false | opus-mt-is-en * source languages: is * target languages: en * OPUS readme: [is-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/is-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | 4476264a855c5689d0abfebc7348a74c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-moaiz_exp1 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6910 - Wer: 0.5549 | bc4d3cd849ee2b38767ce09dcd08c1ac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.7261 | 13.89 | 500 | 2.4864 | 0.9942 | | 1.0036 | 27.78 | 1000 | 0.6910 | 0.5549 | | c1b422609212db18430b23c983dcc456 |
apache-2.0 | ['exbert', 'security', 'cybersecurity', 'cyber security', 'threat hunting', 'threat intelligence'] | false | SecBERT This is the pretrained model presented in [SecBERT: A Pretrained Language Model for Cyber Security Text](https://github.com/jackaduma/SecBERT/), which is a BERT model trained on cyber security text. The training corpus was papers taken from * [APTnotes](https://github.com/kbandla/APTnotes) * [Stucco-Data:... | 047d68614b7b0ba697ec1ce3fee7efb5 |
apache-2.0 | ['exbert', 'security', 'cybersecurity', 'cyber security', 'threat hunting', 'threat intelligence'] | false | **Fill Mask** We proposed to build language model which work on cyber security text, as result, it can improve downstream tasks (NER, Text Classification, Semantic Understand, Q&A) in Cyber Security Domain. First, as below shows Fill-Mask pipeline in [Google Bert](), [AllenAI SciBert](https://github.com/allenai/scib... | e8820f9877dc2c0de3b632bdab6e49f5 |
mit | [] | false | Birb style on Stable Diffusion This is the `<birb-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also... | 0a3fed2216d45a5eb0345c839048cafd |
apache-2.0 | ['translation'] | false | rus-ukr * source group: Russian * target group: Ukrainian * OPUS readme: [rus-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-ukr/README.md) * model: transformer-align * source language(s): rus * target language(s): ukr * model: transformer-align * pre-processing: normalization + Se... | b30a6938d8c907eecc34dfc3ed861799 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: rus-ukr - source_languages: rus - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ru', 'uk'] - src_constituents: {'rus'} - tgt_const... | 664b97334509073b8528d159bac03e8c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000222 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 8e2d7b0061c2de3320742b261767d058 |
apache-2.0 | [] | false | Results The following table summarizes the F1 score obtained as compared to other models and architectures. | Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT | DeepSentiPers | |:------------------------:|:-----------------:|:-----------:|:-----:|:-------------:| | Digikala User Comments | ... | 7b1947f86daecbd11962ab760ee3f229 |
mit | ['generated_from_trainer', 'nlu', 'intent-classification'] | false | multilingual_minilm-amazon-massive-intent This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [MASSIVE1.1](https://huggingface.co/datasets/AmazonScience/massive) dataset. It achieves the following results on the evaluatio... | 1f4450cf0be59a4f0a387d78fe309b37 |
mit | ['generated_from_trainer', 'nlu', 'intent-classification'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 3.7961 | 1.0 | 720 | 3.1657 | 0.3404 | 0.3404 | | 3.1859 | 2.0 | 1440 | 2.4835 | 0.4343 | 0.4343 | | 2.3104 ... | 1b2f37c88c798e0e237e14500ccd9d84 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ft780_class This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9843 - Accuracy: 0.2047 - F1: 0.1823 | 6e95961e9d58006a2d502346ea63154a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 2.1065 | 1.0 | 188 | 2.0425 | 0.1747 | 0.1248 | | 1.9642 | 2.0 | 376 | 1.9959 | 0.1987 | 0.1701 | | 1.9019 |... | 7db5daf08c40ac3af931fd9b1e1d320d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Swahili Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Swahili using the following datasets: - [ALFFA](http://www.openslr.org/25/), - [Gamayun](https://gamayun.translatorswb.org/download/gamayun-5k-english-swahili/) - [IWSLT](https://iw... | b4825efc7087e7a96e101387f1ba7a63 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor processor = Wav2Vec2Processor.from_pretrained("alokmatta/wav2vec2-large-xlsr-53-sw") model = Wav2Vec2For... | b0d39e7ce7e0ba2a9d20534ae804c228 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the foods concept trained by llhbr on the llhbr/dreamboot-pizza dataset. This is a Stable Diffusion model fine-tuned on the foods concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of foods pizza** This model was created as part of the DreamBooth Hackathon 🔥. ... | 4b671ec52d16eddc42848c7c78cc8e16 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Ukrainian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Ukrainian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 763aefe373c3c9d90cbf4e2d239e6067 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "uk", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 0278d2088ace841051e5fcd35f741d03 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Ukrainian test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 68508cb42349ff420c8ae329cadb2026 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/uk.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.e... | d5022e8a7cfa41f7ef9f1163442b0051 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio... | e3d8e068df3a386cc48c0571dd56b53b |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Erzya (myv) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](http... | a446e2c0d6661d758994dbef6a49d8dd |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_r-wav2vec2_s578 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speec... | 3ed8933699b0c9676ba81bebb08be271 |
apache-2.0 | ['roberta', 'classification', 'dialog state tracking', 'conversational system', 'task-oriented dialog'] | false | SetSUMBT-dst-multiwoz21 This model is a fine-tuned version [SetSUMBT](https://github.com/ConvLab/ConvLab-3/tree/master/convlab/dst/setsumbt) of [roberta-base](https://huggingface.co/roberta-base) on [MultiWOZ2.1](https://huggingface.co/datasets/ConvLab/multiwoz21). Refer to [ConvLab-3](https://github.com/ConvLab/Con... | 8c5737b623ef7aa8dd69683efafae864 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-tweet-target-imdb This model is a fine-tuned version of [muhtasham/tiny-mlm-tweet](https://huggingface.co/muhtasham/tiny-mlm-tweet) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4017 - Accuracy: 0.8486 - F1: 0.9181 | 14622cb114716eaedce4a114e0c41811 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5661 | 0.64 | 500 | 0.3869 | 0.8363 | 0.9109 | | 0.3798 | 1.28 | 1000 | 0.3730 | 0.8390 | 0.9125 | | 0.3283 |... | 9c64f44d4492c4e0ec6b9070d83754f7 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1391 - F1: 0.8619 | 5b4baceb33019a7d1bd818a7d8010ea8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2709 | 1.0 | 525 | 0.1825 | 0.7878 | | 0.1298 | 2.0 | 1050 | 0.1373 | 0.8515 | | 0.0825 | 3.0 | 1575 | 0.1391 | 0.8619 | ... | 51fbdb484dd37c0ddb45297e332509c0 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr0.0001-wd0.08-bs64 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2801 - Rmse: 0.5293 - Mse: 0.2801 - Mae: 0.4372 | 17c4184d48008c94a71c864de143ef99 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2799 | 1.0 | 623 | 0.2788 | 0.5280 | 0.2788 | 0.4183 | | 0.2785 | 2.0 | 1246 | 0.2792 | 0.5284 | 0.2792 ... | 489cb49bc65dedaebaa0c0e8ae9aba13 |
apache-2.0 | ['generated_from_trainer'] | false | reddit-bert-text5 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5749 | e2c2bc816d5b57a3132aeadc7c8e1a8b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.0257 | 1.0 | 945 | 2.6167 | | 2.7138 | 2.0 | 1890 | 2.5529 | | 2.6363 | 3.0 | 2835 | 2.5463 | | a42de5f6e6c4bdc253976446d6a85f87 |
mit | [] | false | I Love Chaos on Stable Diffusion This is the `<chaos>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also tr... | c2ee6a160a200b7711b38b92307913a5 |
mit | [] | false | What does this model do? For once it makes everything red :). Once in a while it creates heart shapes, this particular model is best used at the end or after the first 5 to 7 keywords, or deantentuate it using (<chaos>) and enclose the keyword in brackets, for attentuating it more, use <chaos>! with the exclamation ... | 431582b96b68c43cc6b9934128631505 |
mit | [] | false | Example: Prompt Sourced from: > [Prompt Source 'The 100 Most Beautiful Stable Diffusion Prompts'](https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts/) Prompt: new york city, dust storm, cinematic, dramatic, composition, (<chaos>), sunny sky, brutalist, hyper reali... | 71df1b8451e52188e6f364055ea6f658 |
mit | [] | false | Training Data Here is the new concept you will be able to use as a `style` <chaos> :    for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 3e04bdc4cbcd7ba0e1ab4d1b09564734 |
mit | [] | false | How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='Suchinthana/sinhala-gpt-neo') >>> generator("කවියා නුමුහු කළ නුවණ ", do_sample... | 883c2c4dfafb7c928a701a0443f45cb7 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.05, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | 15c26c68d0a88487e55967c20781c4be |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mbart-large-cc25-dequad-qg` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/... | 7a6b2ee35a6c04a47d4a04d2ca993453 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).", list_answer="1855") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mb... | f80703346326388bd9d3e7d30c2dfab5 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-dequad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | | Score | Type | Dataset | ... | 73861eff9330523819c2d2d6b2ce07e5 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_dequad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 32 - epoc... | 43aad0b1d16ba76776458b941ec72460 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Vocoder with HiFIGAN trained on LJSpeech This repository provides all the necessary tools for using a [HiFIGAN](https://arxiv.org/abs/2010.05646) vocoder trained with [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically,... | ee0fbb77167a53c024de57fb91f23f92 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Using the Vocoder ```python import torch from speechbrain.pretrained import HIFIGAN hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmpdir") mel_specs = torch.rand(2, 80,298) waveforms = hifi_gan.decode_batch(mel_specs) ``` | 3b329c1afd419a5e8d5c7cc3e072ba61 |
apache-2.0 | ['Vocoder', 'HiFIGAN', 'text-to-speech', 'TTS', 'speech-synthesis', 'speechbrain'] | false | Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/LJSpeech/T... | 6181d6658069702cf040405850e2d2ce |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Odia This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 or dataset. It achieves the following results on the evaluation set: - Loss: 0.4786 - Wer: 26.6008 | 3d139576affb14203c28ef92452c0a38 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 1000 - mixed_precis... | a2b4c0cfda792874b6abf4caa0c0cbd9 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0001 | 24.01 | 250 | 0.4786 | 26.6008 | | 0.0 | 49.01 | 500 | 0.5252 | 26.9394 | | 0.0 | 74.01 | 750 | 0.5534 | 27.136... | 9c8f96023a0e22c7f887cf239ba54c09 |
apache-2.0 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_wavlm_s662 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 1... | 2e49df8f1f475f2ef298d4d0d6a0b1be |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Dutch Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | 9cff912cf983d81f50dc4988d229ef7c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "nl", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 30a07a8f678cf5b620aafe77b84480ed |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Dutch test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import unidecode import re test_dataset = load_dataset("common_voice", "nl", spl... | 82ccc0e27a941af21f95103329fb2d57 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic` model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\(\)\=\´\–\&\…\—\’]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | 09e616a32123409e9a26433b59574dcc |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = unidecode.unidecode(batch["sentence"]) batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resamp... | 5d25fa1d7082f418ff3619de2aea315d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch... | d2d634d421a7635fb23708ed88af640e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here. | 81d1cc6bcce1b8fd9a6e88bd9f198f1d |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_vp-nl_s44 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your... | 9a96e6c6b9eb0e7475aead3726cd3064 |
apache-2.0 | ['pytorch', 'diffusers', 'text-to-image'] | false | 模型介绍 模型分成四部分： * Text Encoder：把中文文本输入转化成 Embedding 向量 * Latent Diffusion Model：在 Latent 空间中根据文本输入处理随机生成的噪声 * Auto Encoder：将 Latent 空间中的张量还原为图片 * Super Resolution：提升图片分辨率 我们使用中文模型CLIP-ViT-L作为 Text Encoder，使用 [latent-diffusion](https://github.com/CompVis/latent-diffusion) 中的 Auto Encoder，使用 [ESRGAN](https://github.co... | 8b5ed609dd0eb4355694139b93b5a9e0 |
apache-2.0 | ['pytorch', 'diffusers', 'text-to-image'] | false | 使用 基于 Diffusers 开发，请先安装 Diffusers ``` pip install diffusers ``` ```python from LdmZhPipeline import LDMZhTextToImagePipeline generator = LDMZhTextToImagePipeline.from_pretrained("alibaba-pai/pai-diffusion-poem-large-zh") generator.to("cuda") image = generator("远上寒山石径斜 白云深处有人家").images[0] image.save("poem.png") ```... | 52bfa4f650de541d1f0a7e3bb6ffacde |
mit | ['generated_from_trainer'] | false | Fatwa-Topic-Classifier-xlm_roberta This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0214 - Accuracy: 0.6866 - F1 Micro: 0.6866 - F1 Macro: 0.4863 - F1 Weighted: 0.6885 - Precision M... | 2920acc60959b5cdc14763d05f51ab28 |
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